Candidate Gene versus Genome Wide Approaches

After genes in drug response pathways are identified, the next step in the design of a candidate gene association pharmacogenetic study is to identify genetic polymorphisms that are most likely to contribute to the therapeutic and/or adverse responses. There are several databases that contain information on polymorphisms and mutations in human genes (Table 4-1) that allow the investigator to search by gene for reported polymorphisms. Some databases, such as the Pharmacogenetics and Pharmacogenomics Knowledge Base, include phenotypic and genotypic data.

Because it is currently impractical to analyze all polymorphisms in a candidate gene association study, it is important to select polymorphisms that most likely are associated with the drug-response phenotype. For this purpose, there are two categories of polymorphisms. Some polymorphisms do not directly alter function of the expressed protein (e.g., they don't affect the enzyme that metabolizes the drug or the drug receptor). Rather, these polymorphisms are linked to the variant allele that alters function. If they are very tightly linked with the causative polymorphism, these polymorphisms nonetheless may serve as surrogates for drug-response phenotype.

The second type of polymorphism is the causative polymorphism, which directly produces the phenotype. Whenever possible, it is desirable to select polymorphisms for pharmacogenetic studies that are likely to be causative. If biological information indicates that a particular polymorphism alters function, this polymorphism is an excellent candidate to use in an association study.

A potential drawback of the candidate gene approach is that the wrong genes may be studied. Genome-wide approaches, using gene expression arrays, genome-wide scans, or proteomics, can complement the candidate gene approach by providing a relatively unbiased survey of the genome to identify previously unrecognized candidate genes. For example, RNA, DNA, or protein from patients who have unacceptable toxicity from a drug can be compared with corresponding material from identically treated patients who did not exhibit such toxicity. Patterns of gene expression, clusters of polymorphisms or heterozygosity, or relative amounts of proteins can be ascertained using computational tools, to identify genes, genomic regions, or proteins that can be further assessed for germline polymorphisms differentiating the phenotype. Gene expression and proteomic approaches have the advantage that the abundance of signal may itself directly reflect some of the relevant genetic variation; however, both types of expression are highly influenced by the tissue studied, and the most relevant tissue (e.g., brain) may not be readily available. DNA has the advantage that it is readily available and independent of tissue type, but the vast majority of genomic variation is not

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